repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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kingb12/languagemodelRNN | old_comparisons/noing15_LSTM_v_BOW.ipynb | mit | report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_200_512_04drb/encdec_noing15_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_bow_200_512_04drb/encdec_noing15_bow_200_512_04drb.json"]
log_files = ["/Users/bking/IdeaProjects/... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/solutions/1_training_at_scale.ipynb | apache-2.0 | # change these to try this notebook out
PROJECT = <YOUR PROJECT>
BUCKET = <YOUR PROJECT>
REGION = <YOUR REGION>
import os
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = "2.1"
%%bash
gcloud config set project $PROJECT
gcloud config set compute/regi... |
dnc1994/MachineLearning-UW | ml-classification/module-3-linear-classifier-learning-solution.ipynb | mit | import graphlab
"""
Explanation: Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a NumPy array.
Implement the link function for logistic regression.
Write a f... |
mkuron/espresso | doc/tutorials/02-charged_system/02-charged_system-2.ipynb | gpl-3.0 | from espressomd import System, electrostatics, electrostatic_extensions
from espressomd.shapes import Wall
import espressomd
import numpy
"""
Explanation: Tutorial 2: A Simple Charged System, Part 2
7 2D Electrostatics and Constraints
In this section, we use the parametrized NaCl system from the last task to simulate ... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/numpy/numpy.ipynb | mit | import numpy as np
"""
Explanation: NumPy
Credits: Forked from Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel
NumPy Arrays, dtype, and shape
Common Array Operations
Reshape and Update In-Place
Combine Arrays
Create Sample Data
End of explanation
"""
a = np.array([1, 2, 3])
print(a)
print(... |
kit-cel/wt | wt/uebung/u2_schimpanse.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
from random import random, choice
# Kugeln (Werte erstmal unwichtig)
black, red, green, white = 0, 1, 2, 7
# Urnen
kugeln_urne1 = [white, white, white, black, black]
kugeln_urne2 = [white, green, green, red, red]
# Wkeiten
p_urne1 = 0.7
p_urne2 = 0.3 # 1 - p_ur... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, ... |
abulbasar/machine-learning | Scikit - 02 Visualization.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.mlab import normpdf
%matplotlib inline
plt.rcParams['figure.figsize'] = 10, 6
df = pd.read_csv("http://www-bcf.usc.edu/~gareth/ISL/Auto.data", sep=r"\s+")
df.head(10)
df.info()
df["year"].unique()
df.sampl... |
georgetown-analytics/machine-learning | examples/jstyczynski/Phishing Website Classification Analysis.ipynb | mit | %matplotlib inline
import os
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import Ridge, Lasso, ElasticNet
from sklearn.discriminant_ana... |
magnusax/ml-meta-wrapper | examples/gazer-intro-demo.ipynb | mit | import keras
from importlib import reload
import os
import sys
sys.path.insert(0, "C:/Users/magaxels/AutoML")
import gazer; reload(gazer)
from gazer import GazerMetaLearner
"""
Explanation: This notebook demonstrates some very basic functionality currently present in the library
End of explanation
"""
from sklearn.d... |
edinburghlivinglab/dds-notebooks | notebooks/data_formats.ipynb | cc0-1.0 | %%bash
cat ../data/formats/messages.csv
"""
Explanation: Data Formats
Overview
This notebook examines some of the different ways that data is organised within files. We'll call these data formats.
First, we're going to rule out a couple of very widely-used data formats, namely HTML and PDF. The reason is that we are i... |
dssg/diogenes | doc/notebooks/display.ipynb | mit | %matplotlib inline
import diogenes
import numpy as np
wine_data = diogenes.read.open_csv_url('http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
delimiter=';')
"""
Explanation: The Display Module
The :mod:diogenes.display module provides tools for summarizing/exploring d... |
mansweet/GaussianLDA | NIPS Topic Modeling.ipynb | apache-2.0 | import numpy as np
import os
from operator import itemgetter
from collections import Counter
import scipy.stats as stat
from gensim.models import Word2Vec
from nltk import corpus
import FastGaussianLDA2
"""
Explanation: Topic Modeling on NIPS Dataset Using Gaussian LDA w/ Word-Embeddings
End of explanation
"""
wvmod... |
nishantsbi/pattern_classification | machine_learning/scikit-learn/outofcore_modelpersistence.ipynb | gpl-3.0 | import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/rasbt/pattern_classification/master/data/50k_imdb_movie_reviews.csv')
df.tail()
"""
Explanation: Out-of-core Learning and Model Persistence using scikit-learn
When we are applying machine learning algorithms to real-world applications, our compute... |
SHDShim/pytheos | examples/6_p_scale_test_Jamieson_Au.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos
"""
Explanation: 0. General note
This example compares pressure calculated from pytheos and o... |
GoogleCloudPlatform/analytics-componentized-patterns | retail/recommendation-system/bqml-scann/00_prep_bq_and_datastore.ipynb | apache-2.0 | !pip install -q -U apache-beam[gcp]
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
"""
Explanation: Import the sample data into BigQuery and Datastore
This notebook is the first of two notebooks that guide you through completing the pre... |
TwistedHardware/mltutorial | notebooks/quick_tips/5. The largest Prime.ipynb | gpl-2.0 | import numpy as np
import math
from datetime import datetime
%load_ext Cython
"""
Explanation: The largest prime (so far)
A new record for the largest prime has been found lately. Let explore this number and know more about it and how to deal with it using some Python, Numpy and finally using we will take a loog at C... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/Bayesian_Switchpoint_Analysis.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
bspalding/research_public | lectures/drafts/Risk analysis.ipynb | apache-2.0 | # Import the libraries we'll be using
import numpy as np
import statsmodels.api as sm
from statsmodels import regression
import matplotlib.pyplot as plt
# Get market cap and book-to-price for all assets in universe
fundamentals = init_fundamentals()
data = get_fundamentals(query(fundamentals.valuation.market_cap,
... |
aeantipov/fk_mc | examples/Hamiltonian.ipynb | gpl-2.0 | import importlib
import os
import sys
def is_interactive():
import __main__ as main
return not hasattr(main, '__file__')
# defaults
shell_mode = not is_interactive()
plot_graphs = importlib.util.find_spec("matplotlib") is not None and not shell_mode
# Matplotlib
if (plot_graphs):
import matplotlib
imp... |
JackDi/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
pkreissl/espresso | doc/tutorials/raspberry_electrophoresis/raspberry_electrophoresis.ipynb | gpl-3.0 | import espressomd
import espressomd.interactions
import espressomd.electrostatics
import espressomd.lb
import espressomd.virtual_sites
import sys
import tqdm
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
espressomd.assert_features(["ELECTROSTATICS", "ROTATION", "ROTATIONAL_INERTIA", "EXTER... |
dnc1994/MachineLearning-UW | ml-classification/blank/module-10-online-learning-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
Extract features from Amazon product reviews.
Convert an SFrame into a Num... |
quaquel/EMAworkbench | ema_workbench/examples/scenario_discovery_resampling.ipynb | bsd-3-clause | import pandas as pd
data = pd.read_csv("./data/bryant et al 2010 data.csv", index_col=False)
x = data.iloc[:, 2:11]
y = data.iloc[:, 15].values
"""
Explanation: Performing Scenario Discovery in Python
The purpose of example is to demonstrate how one can do scenario discovery in python. I will demonstrate how we can p... |
KECB/learn | BAMM.101x/text_mining.ipynb | mit | def get_words(url):
import requests
words = requests.get(url).content.decode('latin-1')
word_list = words.split('\n')
index = 0
while index < len(word_list):
word = word_list[index]
if ';' in word or not word:
word_list.pop(index)
else:
index+=1
re... |
computational-class/cjc2016 | code/08.03-probability.ipynb | mit | from collections import Counter
import math, random
import matplotlib.pyplot as plt # pyplot
def random_kid():
return random.choice(["boy", "girl"])
random_kid()
both_girls = 0
older_girl = 0
either_girl = 0
random.seed(100)
for _ in range(10000):
younger = random_kid()
older = random_kid()
if older... |
utensil/julia-playground | py/galgebra_gr_metrics.ipynb | mit | minkowski = build_ga_from_solution(Solution().minkowski())
show(minkowski.mv())
minkowski.g
minkowski.e_sq
dot_basis_r_basis(minkowski)
gg(minkowski)
"""
Explanation: Minkowski Spacetime Metric
End of explanation
"""
g4coords = (u, x, y, z) = symbols("u x y z")
g = [
[0, 0, -exp(-z), 0],
[0, HALF * u **... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Logic_functions_Solutions.ipynb | gpl-3.0 | import numpy as np
np.__version__
"""
Explanation: Logic functions
End of explanation
"""
x = np.array([1,2,3])
print np.all(x)
x = np.array([1,0,3])
print np.all(x)
"""
Explanation: Truth value testing
Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to F... |
whiterd/Tutorial-Notebooks | 2019-06-09-Presentation-Jupyter-Notebook.ipynb | mit | some_global_variable = 6
def my_function(arg):
"""This is a docstring."""
some_global_variable = 1
return some_global_variable
print(my_function(5))
some_global_variable
int?
*int*?
"""
Explanation: <img src="resources/jupyter-main-logo.svg" alt="Jupyter" height="200" width="200">
<h1 align="... |
griffinfoster/fundamentals_of_interferometry | 7_Observing_Systems/7_8_rfi.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
7. Observing Systems
Previous: 7.7 Propagation Effects
Next: 7.x Further Reading and References
Import standard modules:
End of ... |
esimonov/chess_prob | 01-Square Boards(RUS).ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from string import ascii_uppercase as alphabet
"""
Explanation: О вероятности попасть под удар фигуры, поставленной случайным образом на шахматную доску
На шахматную доску случайным образом поставлены две фигуры. С какой вероятностью первая фигура б... |
StefanoAllesina/ISC | regex/solutions/BeeChecklist_solution.ipynb | gpl-2.0 | import csv
import re
"""
Explanation: Solution of Bee Checklist
First of all, we import the two modules we'll need to read the csv file, and to use regular expressions:
End of explanation
"""
with open('../data/bee_list.txt') as f:
csvr = csv.DictReader(f, delimiter = '\t')
species = []
authors = []
... |
njtwomey/ADS | 03_data_transformation_and_integration/02_worldbank.ipynb | mit | PYTHON_VERSION = 3 ## change to 2 if using Python 2.x
DOWNLOAD_DATA = False ## change to True if you do not have the csv files on your machine
import pandas as pd
from zipfile import ZipFile
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from IPython import display
from time import sleep
%m... |
tensorflow/docs-l10n | site/en-snapshot/lattice/tutorials/premade_models.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
enbanuel/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx01.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='Quartz_death_penalty_chart.0.png')
"""
Explanation: Graphical excellence and integrity
Find a data-focused visualiza... |
DTUWindEnergy/TOPFARM | examples/notebook/tutorial1.ipynb | agpl-3.0 | # FUSEDWind imports
from fusedwind.plant_flow.vt import GenericWindFarmTurbineLayout, WTPC, WeibullWindRoseVT, GenericWindRoseVT
# Topfarm lib imports
from topfarm.aep import AEP
from topfarm.layout_distribution import spiral, DistributeSpiral, DistributeXY, DistributeFilledPolygon
from topfarm.plot import OffshorePl... |
google-research/google-research | sufficient_input_subsets/tutorials/sis_mnist_tutorial.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
SimplifyData/Codality | Demo-Test.ipynb | mit | Ax = [-1, 3, -4, 5, 1, -6, 2, 1]
"""
Explanation: Write a function:
def solution(A)
that, given a zero-indexed array A consisting of N integers, returns any of its equilibrium indices. The function should return −1 if no equilibrium index exists.
For example, given array A shown above, the function may return 1, 3 or... |
AllenDowney/ModSimPy | soln/jump_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
albahnsen/ML_SecurityInformatics | notebooks/11-SVM.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('fivethirtyeight')
"""
Explanation: 11 - Support Vector Machines
by Alejandro Correa Bahnsen
version 0.2, May 2016
Part of the class Machine Learning for Security Informatics
This notebook is licensed under a Cr... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-veg-lr/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-veg-lr', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-VEG-LR
Topic: Atmos
S... |
whitead/numerical_stats | project/type1_examples/bikes.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import scipy.stats as ss
import seaborn as sns
import scipy.optimize
sns.set_style('whitegrid')
sns.set_context('notebook')
#the data is read in from the csv
all_data = pd.read_csv('Dengler_L_Project/tra... |
hwagit/jupyter-notebooks | mod-phase-trans-metals-alloys/tmt4260_exercise1.ipynb | mit | %matplotlib inline
import numpy as np
import scipy.integrate as sci
import scipy.special as scs
import matplotlib.pyplot as plt
import math
import warnings
warnings.filterwarnings('ignore')
fsize = 15
newparams = {'axes.titlesize': fsize, 'axes.labelsize': fsize,
'axes.linewidth': 2, 'savefig.dpi': 200,
... |
tensorflow/neural-structured-learning | research/carls/examples/notebooks/carls_mlp_cora.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed u... |
tensorflow/docs-l10n | site/zh-cn/tutorials/structured_data/preprocessing_layers.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
marwin-ko/projects | gyant-technical_challenge/word2vec_model.ipynb | mit | from gensim.models import Word2Vec
from sklearn.manifold import TSNE
from nltk.corpus import genesis
import matplotlib.pyplot as plt
from textblob import TextBlob
from pprint import pprint
import pandas as pd
import numpy as np
import logging
import csv
import re
% matplotlib inline
logging.basicConfig(format='%(asctim... |
nadvamir/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
CAChemE/curso-python-datos | notebooks/015-NumPy-EntradaSalida.ipynb | bsd-3-clause | !head ../data/temperaturas.csv # Esta línea no funciona en Windows
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
datos = np.loadtxt("../data/temperaturas.csv",
skiprows=1, # Saltamos una línea
usecols=(1, 2, 3), # Solo columnas 2, 3 y 4
... |
robertoalotufo/ia898 | src/idft.ipynb | mit | import numpy as np
def idft(F):
import ia898.src as ia
s = F.shape
if F.ndim == 1: F = F[np.newaxis,np.newaxis,:]
if F.ndim == 2: F = F[np.newaxis,:,:]
(p,m,n) = F.shape
A = ia.dftmatrix(m)
B = ia.dftmatrix(n)
C = ia.dftmatrix(p)
Faux = np.conjugate(A).dot(F)
Faux = Faux.dot(... |
scanner-research/scanner | examples/Walkthrough.ipynb | apache-2.0 | %%html
<video width="560" height="315" src="https://storage.googleapis.com/scanner-data/public/sample-clip.mp4?ignore_cache=1" controls />
"""
Explanation: Scanner walkthrough
To explore how Scanner fits in to a bigger pipeline, we're going to walk through a simple video analysis application. If you want to analyze a ... |
desihub/desitarget | doc/nb/mock-qa.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white', font_scale=1.1, palette='Set2')
"""
Explanation: Mock QA
This notebook visualizes the default set of mocks currently (as of 2018 August) being used in the DESI Data Challenges.
End of explanation
"""
from desit... |
raschuetz/foundations-homework | Data_and_Databases_homework/07/Fake Drake/Fake Drake Lyrics Generator.ipynb | mit | from bs4 import BeautifulSoup
from urllib.request import urlopen
html_str = urlopen ('http://www.azlyrics.com/d/drake.html').read()
document = BeautifulSoup(html_str, 'html.parser')
"""
Explanation: Plan of Action
* load the lyrics into a Data Frame. Every couplet will be in a line.
[
{
'couplet': """You ... |
dlsun/symbulate | docs/rv.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Symbulate Documentation
Random Variables
A random variable is a function that takes as an input an outcome of a probability space and returns a real number. Commonly encountered random variables are either discrete or continuous.
* A discrete random variab... |
quantopian/research_public | notebooks/lectures/Spearman_Rank_Correlation/questions/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
"""
Explanation: Exercises: Spearman Rank Correlation
Lecture Link
This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.
https://www.quantopian.com/lectures#S... |
tensorflow/docs-l10n | site/ko/lite/examples/jax_conversion/overview.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
agushman/coursera | src/cours_2/week_1/PA_linreg_stochastic_grad_descent.ipynb | mit | def write_answer_to_file(answer, filename):
with open(filename, 'w') as f_out:
f_out.write(str(round(answer, 3)))
"""
Explanation: Линейная регрессия и стохастический градиентный спуск
Задание основано на материалах лекций по линейной регрессии и градиентному спуску. Вы будете прогнозировать выручку компан... |
woutdenolf/spectrocrunch | doc/source/tutorials/polarization.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from spectrocrunch.sources import polarization
"""
Explanation: Polarization
Following Detlefs[2012] the electric field of a monochromatic plane wave can be described as
$$
\begin{equation}
\begin{split}
\vec{E}(t,\vec{x}) =& \Re[(V_0\hat{e}_0 + V_... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a/td2a_visualisation.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.data - Matplotlib
Tutoriel sur matplotlib.
End of explanation
"""
#Pour intégrer les graphes à votre notebook, il suffit de faire
%matplotlib inline
#ou alors
%pylab inline
#pylab charge également numpy. C'est la commande du calcul ... |
DANA-Laboratory/CoolProp | doc/notebooks/Saturation and Acentric Factor.ipynb | mit | import CoolProp, numpy as np, matplotlib.pyplot as plt
%matplotlib inline
PropsSI = CoolProp.CoolProp.PropsSI
for fluid in ['Ethane','Water','R125']:
Tc = PropsSI('Tcrit',fluid)
pc = PropsSI('pcrit',fluid)
Ts = np.linspace(PropsSI('Ttriple',fluid),Tc)
ps = PropsSI('P','T',Ts,'Q',0,fluid)
plt.plot(Tc... |
nonmean/nonmean.github.io | _notebooks/2020-02-21-introducing-fastpages.ipynb | mit | #hide
!pip install pandas altair
#collapse-hide
import pandas as pd
import altair as alt
"""
Explanation: Introducing fastpages
An easy to use blogging platform with extra features for <a href="https://jupyter.org/">Jupyter Notebooks</a>.
toc: true
badges: true
comments: true
sticky_rank: 1
author: Jeremy Howard ... |
liuhanfei0615/liupengyuan.github.io | chapter2/homework/computer/middle/201611680672.ipynb | mit | ch=input('请输入一个字符: ')
n=int(input('请输入打印行数：'))
for i in range(1,n+1):
print(' '*(n-i)+(ch+' ')*i)
"""
Explanation: 1、写函数，给定符号和行数，如’*’，5，可打印相应行数的如下图形：
End of explanation
"""
for i in range(1,10):
for j in range(1,i+1):
print('{}*{}={:<2}'.format(i,j,i*j),end=' ')
print()
"""
Explanation: 2、打印如下9... |
griffinfoster/fundamentals_of_interferometry | 2_Mathematical_Groundwork/fft_implementation_assignment.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
import cmath
"""
Explanation: Implementation of a Radix-2 Fast Fourier Transform
Import standard modules:
End of explanation
"""
def loop_DFT(x):
"""
Impleme... |
elmaso/tno-ai | aind2-dl-master/IMDB_In_Keras_Solutions.ipynb | gpl-3.0 | # Imports
import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
"""
Explanation: Analyzing IMDB Data in... |
jovanbrakus/data-analysis-tools | week-4-assignment.ipynb | gpl-3.0 | import pandas
import numpy
import scipy.stats
import seaborn
import matplotlib.pyplot as plt
data = pandas.read_csv('nesarc_pds.csv', low_memory=False)
# MAJORDEPLIFE - Diagnosed major depressions in lifetime
# S2AQ5A - Drink beer in last 12 months
# S2AQ5B - How often drank a beer in last year
data['MAJORDEPLIFE'] ... |
DeepLearningUB/DeepLearningMaster | 13. Reinforcement learning.ipynb | mit | import numpy as np
import seaborn
class Bandit:
def __init__(self):
self.arm_values = np.random.normal(0,1,10)
self.K = np.zeros(10)
self.est_values = np.zeros(10)
def get_reward(self,action):
noise = np.random.normal(0,1)
reward = self.arm_values[action] + noi... |
Naereen/notebooks | euler/Project Euler (Python 3) - to problem 50.ipynb | mit | %load_ext Cython
from typing import List
def divisors(n: int) -> List[int]:
return [k for k in range(1, n+1) if n%k == 0]
divisors(28)
def number_of_divisors(n: int) -> int:
return len(divisors(n))
number_of_divisors(28)
def int_sqrt(n: int) -> int:
if n <= 0: return 0
if n == 1: return 1
i = ... |
cathywu/flow | tutorials/tutorial05_visualize.ipynb | mit | ! python ../../../rllab/viskit/frontend.py /path/to/result/directory
"""
Explanation: Tutorial 05: Visualizing Experiment Results
This tutorial describes the process of visualizing and replaying the results of Flow experiments run using RL. The process of visualizing results breaks down into two main components:
rew... |
tensorflow/docs-l10n | site/en-snapshot/lite/performance/post_training_float16_quant.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week6/.ipynb_checkpoints/module-9-precision-recall-assignment-graphlab-checkpoint.ipynb | mit | import graphlab
from __future__ import division
import numpy as np
graphlab.canvas.set_target('ipynb')
"""
Explanation: Exploring precision and recall
The goal of this second notebook is to understand precision-recall in the context of classifiers.
Use Amazon review data in its entirety.
Train a logistic regression m... |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/.ipynb_checkpoints/Day1_rep10_justOverlap-checkpoint.ipynb | mit | import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%load_ext pushnote
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66
"""
Explanation: Goal
Simulating fullCyc Day1 control gra... |
kunaltyagi/SDES | notes/python/p_norvig/algo/Probability.ipynb | gpl-3.0 | from fractions import Fraction
def P(event, space):
"The probability of an event, given a sample space of equiprobable outcomes."
return Fraction(len(event & space),
len(space))
"""
Explanation: <div style="text-align: right">Peter Norvig, 12 Feb 2016</div>
A Concrete Introduction to Pr... |
bmeaut/python_nlp_2017_fall | course_material/06_Decorators_Packaging/06_Numpy.ipynb | mit | import numpy
"""
Explanation: Please fill out the feedback form:
https://docs.google.com/forms/d/e/1FAIpQLScORHXiMgjvLJDl35SFPA1uXF8J8kSMxV6SkcZSQBRvyhj0CA/viewform?usp=sf_link
And now for something completely different...
Python for mathematics, science and engineering
https://scipy.org/
Numpy
numpy is a python packa... |
andrew-lundgren/detchar | Notebooks/dft_conventions_and_normalization.ipynb | gpl-3.0 | invasd = sqrt(2./srate)/sqrt(shaped_psd1)
invasd[0] = 0.
whitener = sig.hann(seglen*srate)*roll(irfft(invasd), seglen*srate/2)
wtest = TimeSeries(sig.convolve(shaped_noise2, whitener, mode='same')[8*srate:-8*srate],
sample_rate=srate)
p1 = wtest.psd(seglen).plot()
mean(wtest.psd(seglen)) / (2.... |
scottkleinman/WE1S | we1s-test/ipython-notebook-test.ipynb | mit | ##### Configuration #####
# Configure the filename
filename = "file.txt"
# Configure the path to input directory
input_path = "C:\Users\USERNAME\Desktop\we1s-test\input"
# input_path = "/Users/USERNAME/Desktop/we1s-test/input"
# Configure the path to output directory
output_path = "C:\Users\USERNAME\Desktop\we1s-tes... |
bp-kelley/rdkit | Contrib/CalcLigRMSD/Examples_CalcLigRMSD.ipynb | bsd-3-clause | help(CalcLigRMSD)
"""
Explanation: CalcLigRMSD
CalcLigRMSD calculates the Root-mean-square deviation (RMSD) between two prealigned molecules.
This jupyter notebook shows examples of how to use the CalcLigRMSD function.
This function is particularly useful in 2 cases:
when the atom names in the compared structures d... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/1D/FD_1D_DX8_DT2_fast.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
"""
Explanation: FD_1D_DX8_DT2_fast 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the first-order acoustic wav... |
yl565/statsmodels | examples/notebooks/robust_models_0.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
"""
Explanation: Robust Linear Models
End of explanation
"""
data = sm.datasets.stackloss.load()
data.exog = ... |
jdhp-docs/python_notebooks | nb_dev_python/python_pyomo_stock_problem_1.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from pyomo.environ import *
"""
Explanation: Pyomo - Stock problem v1
Pyomo installation: see http://www.pyomo.org/installation
pip install pyomo
End of explanation
"""
# Cost of energy on the market
#cost = [10, 30, 20] # -> -100, 100, 0
#cost = [10, 30... |
aje/POT | notebooks/plot_gromov.ipynb | mit | # Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import scipy as sp
import numpy as np
import matplotlib.pylab as pl
from mpl_toolkits.mplot3d import Axes3D # noqa
import ot
"""
Explanation: Gromov-Wasserstein example
This example is designed to s... |
RNAer/Calour | doc/source/notebooks/microbiome_machine_learning.ipynb | bsd-3-clause | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import RepeatedStratifiedKFold
from calour.training import plot_scatter, plot_roc, plot_cm
import calour as ca
%matplotlib notebook
"""
Explanation: Microbiome machine learning analysis
Setup
Import the calour mod... |
phanrahan/magmathon | examples/uart/uart.ipynb | mit | import magma as m
m.set_mantle_target("ice40")
from mantle import Register
@m.circuit.combinational
def txmod_logic(
data : m.Bits(8),
writing : m.Bit,
valid : m.Bit,
dataStore : m.Bits(11),
writeClock : m.Bits(14),
writeBit : m.Bits(4),) -> (m.Bit,
... |
evidation-health/pymc3 | pymc3/examples/GLM-hierarchical.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import pandas as pd
data = pd.read_csv('data/radon.csv')
county_names = data.county.unique()
county_idx = data['county_code'].values
n_counties = len(data.county.unique())
"""
Explanation: The best of both worlds: Hierarchical ... |
pombredanne/gensim | docs/notebooks/deepir.ipynb | lgpl-2.1 | # ### uncomment below if you want...
# ## ... copious amounts of logging info
# import logging
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
# rootLogger = logging.getLogger()
# rootLogger.setLevel(logging.INFO)
# ## ... or auto-reload of gensim during development
# %load... |
khalido/algorithims | k nearest neighbours.ipynb | gpl-3.0 | iris = sns.load_dataset("iris")
print(f"Iris dataset shape: {iris.shape}")
iris.head()
"""
Explanation: K Nearest Neighbours is a algorithim for finding out the similarity or distance b/w two things, to find out how alike/different they are.
Say we have a bunch of fruit, KNN will classify them into clusters by using ... |
gxxjjj/QuantEcon.py | solutions/uncertainty_traps_solutions.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import quantecon as qe
import seaborn as sns
import itertools
"""
Explanation: quant-econ Solutions: Uncertainty Traps
Solutions for http://quant-econ.net/py/uncertainty_traps.html
End of explanation
"""
palette = ... |
Aggieyixin/cjc2016 | code/04.PythonCrawlerGovernmentReport.ipynb | mit | import urllib2
from bs4 import BeautifulSoup
from IPython.display import display_html, HTML
HTML('<iframe src=http://www.hprc.org.cn/wxzl/wxysl/lczf/ width=1000 height=500></iframe>')
# the webpage we would like to crawl
"""
Explanation: 数据抓取：
抓取47年政府工作报告
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-co... |
empet/Math | Julia-set.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def Julia(z,c, Miter=80):#Miter, maximum number of allowed iterations
for n in range(Miter):
if abs(z)>2:
return n
z = z*z + c
return Miter
"""
Explanation: Escape Time Algorithm for Plotting Julia Set
L... |
tensorflow/docs-l10n | site/zh-cn/tutorials/images/transfer_learning.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
AC209ConsumerConfidence/AC209ConsumerConfidence.github.io | DataCleaning_Final.ipynb | gpl-3.0 | all_data_list = []
for year in range(1990,2017):
data = pd.read_csv('{}_Output.csv'.format(year), header=None)
all_data_list.append(data) # list of dataframes
data = pd.concat(all_data_list, axis=0)
data.columns = ['id','date','headline', 'lead']
data.head()
"""
Explanation: <hr width=80%>
<center> Data Cleani... |
grmToolbox/grmpy | promotion/grmpy_tutorial_notebook/tutorial_semipar_notebook.ipynb | mit | import numpy as np
from tutorial_semipar_auxiliary import plot_semipar_mte
from grmpy.estimate.estimate import fit
import warnings
warnings.filterwarnings('ignore')
%load_ext autoreload
%autoreload 2
"""
Explanation: Replication of Carneiro, Heckman, & Vytlacil's (2011) Local Instrumental Variables approach
In thi... |
california-civic-data-coalition/python-calaccess-notebooks | calaccess-exploration/first-python-notebook-data-preparation.ipynb | mit | import os
import requests
from datetime import datetime
from clint.textui import progress
import pandas
pandas.set_option('display.float_format', lambda x: '%.2f' % x)
pandas.set_option('display.max_columns', None)
"""
Explanation: "First Python Notebook" data preparation
By Ben Welsh
This notebook prepares a dataset... |
jdvelasq/ingenieria-economica | 09-conversion-monedas.ipynb | mit | # Importa la librería financiera.
# Solo es necesario ejecutar la importación una sola vez.
import cashflows as cf
"""
Explanation: Conversión de Monedas
Juan David Velásquez Henao
jdvelasq@unal.edu.co
Universidad Nacional de Colombia, Sede Medellín
Facultad de Minas
Medellín, Colombia
Haga click aquí para acceder a... |
OceanPARCELS/parcels | parcels/examples/tutorial_periodic_boundaries.ipynb | mit | %matplotlib inline
from parcels import FieldSet, ParticleSet, JITParticle, plotTrajectoriesFile
from parcels import AdvectionRK4
from datetime import timedelta as delta
import math
"""
Explanation: Tutorial on how to implement periodic boundaries
This tutorial will show how to implement Periodic boundary conditions (w... |
probml/pyprobml | notebooks/book2/09/vb_gmm_tfp.ipynb | mit | import functools
import numpy as np
try:
import tensorflow.compat.v2 as tf
except ModuleNotFoundError:
%pip install -qq tensorflow
import tensorflow.compat.v2 as tf
try:
import tensorflow_probability as tfp
except ModuleNotFoundError:
%pip install -qq tensorflow-probability
import tensorflow_pr... |
phoebe-project/phoebe2-docs | 2.0/examples/mesh_wd.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
"""
Explanation: Wilson-Devinney Style Meshing
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanat... |
TareasJF/INF393 | T2/Pregunta1.ipynb | mit | # Generacion de los datos para analisis
from sklearn.utils import check_random_state
import matplotlib.patches as mpatches
import numpy as np
def build_data(seed, noise_seed=64, n_samples=500, noise=20):
n_samples=500
mean = (0,-4)
C = np.array([[0.3, 0.1], [0.1, 1.5]])
np.random.seed(seed)
datos1... |
bkuczenski/lca-tools-datafiles | doc/Jaccard Index matching example.ipynb | gpl-3.0 | stopWordsFile = "en.txt"
with open(stopWordsFile) as f:
stoplist = [x.strip('\n') for x in f.readlines()]
"""
Explanation: Load in the stopwords file. These are common words which we wish to exclude when performing comparisons (a, an, the, etc). Every word is separated by a new line.
End of explanation
"""
# htt... |
axbaretto/beam | examples/notebooks/documentation/transforms/python/elementwise/partition-py.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you u... |
Kaggle/learntools | notebooks/deep_learning/raw/ex5_data_augmentation.ipynb | apache-2.0 | from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D
num_classes = 2
resnet_weights_path = '../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
my_new_model = Sequential()
my_new... |
aakashm301/Workshop | Refactored_Py_DS_ML_Bootcamp-master/09-Geographical-Plotting/03-Choropleth Maps Exercise - Solutions.ipynb | gpl-3.0 | import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot,plot
init_notebook_mode(connected=True)
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Choropleth Maps Exercise - Solutions
Welcome to the Choropleth Maps Exercise! In this exercise ... |
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